For 3D upsampling, Chen et al. [1]} trained a CNN with 3D convolutions and used a GAN–based loss function to produce sharper and more realistic images. In order to upsample a 3D image, Du et al. [2]} employed a deconvolutional layer composed of 3D filters to upsample the LR image, in an attempt to reduce the computational complexity. As [1]}, [2]}, [5]}, [6]}, we tackle the problem of 3D CT/MRI image super-resolution. However, instead of using inefficient 3D filters to upsample the LR images in a single step, we propose a more efficient two-stage approach that uses 2D filters. Our approach employs a CNN to increase the resolution in width and height, and another CNN to further increase the resolution depth-wise.
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